摘要
针对线性PCA无法提取R&D绩效系统中重要的非线性绩效隐患信息的弊端,首先运用RBF网络实现R&D绩效警兆指标变量非线性主元(警情指标变量)的识别,并通过Q统计和SPE贡献图法实现R&D绩效隐患的分离和预警。然后运用Dephi专家评判法校正警情指标得分,并构建BP神经网络获得企业在没有绩效隐患情况下应该达到的警兆指标基准得分,从而发现偏差,采取有针对性的控制措施防患于未然。实证研究表明,该套模型与方法可以克服线性PCA在提取绩效警兆信息(观测变量)的非线性特征方面存在的不足,并能够较为准确地实现R&D绩效隐患的分离、预警和控制。
Firstly, nonlinear principal components in predictive variables of R&D performance distress are abstracted by using RBF neural network, and R&D performance distress is separated and warned by using Q statistical method and SPE contributive graph. Secondly, this paper modifies the scores of warning variables by using Dephi method and build BP neural network to get the scores of predictive variables without performance distress, then, finds the divagation and adopts special measures to modify them. The investigation has proved that this model and method not only can extract the nonlinear features compared with the linear Principal Component Analysis method, but also predict and control R&D performance distress very well in high-tech enterprises.
出处
《系统工程理论方法应用》
2004年第5期419-424,共6页
Systems Engineering Theory·Methodology·Applications
基金
国家自然科学基金资助项目(70272029)
关键词
绩效控制
绩效隐患
RBF神经网络
BP神经网络
performance control
performance distress
RBF neural network
BP neural network
